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The detection of selection signatures assists in understanding domestication, evolution, and the identification of genomic regions related to adaptation and production traits in buffaloes. The emergence of high-throughput technologies like Next Generation Sequencing and SNP genotyping had expanded our ability to detect these signatures of selection. In this study, we sought to identify signatures of selection in five buffalo populations (Brazilian Murrah, Bulgarian Murrah, Indian Murrah, Nili-Ravi, and Kundi) using Axiom Buffalo 90 K Genotyping Array data. Using seven different methodologies (Tajima's D, CLR, ROH, iHS, FST, FLK and hapFLK), we identified selection signatures in 374 genomic regions, spanning a total of 381 genes and 350 quantitative trait loci (QTLs). Among these, several candidate genes were associated with QTLs for milk production, reproduction, growth and carcass traits. The genes and QTLs reported in this study provide insight into selection signals shaping the genome of buffalo breeds. Our findings can aid in further genomic association studies, genomic prediction, and the implementation of breeding programmes in Indian buffaloes. [ABSTRACT FROM AUTHOR]

The field of genetics has evolved a lot after the emergence of molecular and advanced genomic technologies. The advent of Next Generation Sequencing, SNP genotyping platforms and simultaneous reduction in the cost of sequencing had opened the door to genomic research in farm animals. There are various applications of genomics in livestock, such as the use of genomic data: (i) to investigate genetic diversity and breed composition/population structure (ii) to identify genetic variants and QTLs related to economically important and ecological traits, genome-wide association studies (GWAS) and genomic signatures of selection; (iii) to enhance breeding programs by genomic selection. Compared to traditional methods, genomic selection is expected to improve selection response by increasing selection accuracy and reducing the generation interval due to early selection. Genomic selection (GS) in developed countries has led to rapid genetic gains, especially in dairy cattle, due to a well-established genetic evaluation system. Indian livestock system is still lagging behind developed nations in adopting these technologies. This review discusses the current status, challenges, and future perspectives of livestock genomics in India. [ABSTRACT FROM AUTHOR]

Copy number variations (CNVs) are major forms of genetic variation with an increasing importance in animal genomics. This study used the Illumina BovineSNP 50 K BeadChip to detect the genome-wide CNVs in the Tharparkar cattle. With the aid of PennCNV software, we noticed a total of 447 copy number variation regions (CNVRs) across the autosomal genome, occupying nearly 2.17% of the bovine genome. The average size of detected CNVRs was found to be 122.2 kb, the smallest CNVR being 50.02 kb in size, to the largest being 1,232.87 Kb. Enrichment analyses of the genes in these CNVRs gave significant associations with molecular adaptation-related Gene Ontology (GO) terms. Most CNVR genes were significantly enriched for specific biological functions; signaling pathways, sensory responses to stimuli, and various cellular processes. In addition, QTL analysis of CNVRs described them to be linked with economically essential traits in cattle. The findings here provide crucial information for constructing a more comprehensive CNVR map for the indigenous cattle genome. [ABSTRACT FROM AUTHOR]

Knowledge about genetic diversity is very essential for the management and sustainable utilization of livestock genetic resources. In this study, we presented a comprehensive genome-wide analysis of genetic diversity, ROH, inbreeding, linkage disequilibrium, effective population size and haplotype block structure in Tharparkar cattle of India. A total of 24 Tharparkar animals used in this study were genotyped with Illumina BovineSNP50 array. After quality control, 22,825 biallelic SNPs were retained, which were in HWE, MAF > 0.05 and genotyping rate >90%. The overall mean observed (HO) and expected heterozygosity (HE) were 0.339 ± 0.156 and 0.325 ± 0.129, respectively. The average minor allele frequency was 0.234 with a standard deviation of ± 0.131. We identified a total of 1832 ROH segments and the highest autosomal coverage of 13.87% was observed on chromosome 23. The genomic inbreeding coefficients estimates by FROH, FHOM, FGRM and FUNI were 0.0589, 0.0215, 0.0532 and 0.0160 respectively. The overall mean linkage disequilibrium (LD) for a total of 133,532 pairwise SNPs measured by D' and r2 was 0.6452 and 0.1339, respectively. In addition, we observed a gradual decline in effective population size over the past generations. [ABSTRACT FROM AUTHOR]

Epigenetics refers to the study of heritable phenotypic changes without alterations in the DNA sequence. Advanced studies in epigenetic molecular mechanisms and their biological functions have relied on various model systems including bacteria, fungi, plants, insects, and mammals. The fruit fly Drosophila melanogaster has been an established model organism in the study of genetics and developmental biology. Many regulatory pathways are conserved in Drosophila compared to mammals, rendering it a powerful model to study epigenetic mechanisms. In this review, we outline the various epigenetic mechanisms and their importance in Drosophila chiefly focussing on the predominant epigenetic variations such as DNA methylation, histone modifications like H3K18 acetylation, H3K27 and H4K20 methylation, H3S10 phosphorylation, H2K118 ubiquitylation, H4 sumoylation, RNA associated silencing, polycomb proteins, PEV (position effect variegation), TPE (Telomeric position effect), and dosage compensation. All these types of epigenetic mechanisms can be studied precisely in Drosophila by inducing various mutations in the genome. Advanced studies in Drosophila epigenetics helps to understand genomic imprinting, neurological memory, circadian rhythms, aging, and carcinogenesis. [ABSTRACT FROM AUTHOR]

The analysis of genetic diversity is very essential to understand the evolution and adaptability of livestock species to different environmental conditions. Genetic variation is the raw material for selection and foundation for breed improvement programmes. The use of high throughput molecular marker technologies, advanced statistical and software tools have revolutionized the precision of animal genetic diversity analysis in a large number of populations with limited time and resources. SNP genotyping is one of the best methods to study genetic variation due to its abundance in livestock species, stability and cost-effectiveness. In this review, some of the most popularly used diversity analysis software programs in livestock genomics are discussed briefly. Microsatellite-based software such as Genepop, Fstat, Genetix, TFPGA, Stackpack, Arlequin and other multipurpose software such as PLINK, PowerMarker, TASSEL, BEAGLE, R software packages (GenABEL, SNPassoc, snpstats, SNP relate), Gemma, SNP Express, SVS, etc. can be utilized for diversity analysis. [ABSTRACT FROM AUTHOR]